2D and 3D Keypoint Detection. Estimation of a human pose is a vital task in computer vision, and a popular pose representation is human skeletal keypoints. The field is categorized by output format: 2D [1]}, [2]}, [3]}, [4]} and 3D [5]}, [6]}, [7]}, [8]}, [9]}, [10]} keypoint detection, or by the number of views: single-view [1]}, [2]}, [3]}, [7]}, [5]}, [6]}, [9]} and multi-view pose estimation [18]}, [19]}, [20]}.
For 2D keypoint detection, single-frame datasets such as MPII [21]} and COCO [22]} provide diverse images with 2D keypoints annotations, whereas video datasets such as J-HMDB [23]}, Penn Action [24]} and PoseTrack [25]} provide sequences of 2D keypoints. However, they lack 3D ground truths. In contrast, 3D keypoint datasets are typically built indoor data to accommodate sophisticated equipment, such as Human3.6M [26]}, CMU Panoptic [27]}, MPI-INF-3DHP [28]}, TotalCapture [29]}, and AIST++ [30]}. Compared to these datasets, HuMMan not only supports 2D and 3D keypoint detection but also textured mesh reconstruction assist in more holistic modeling of humans.
